Impact of prognostic nutritional index on survival in periampullary/pancreatic cancer patients undergoing pylorus-preserving pancreaticoduodenectomy: A propensity score-matched analysis.

Background: Although the prognostic nutritional index (PNI) may predict surgical outcomes in certain cancers, the impact of PNI on surgical prognosis in patients undergoing pylorus-preserving pancreaticoduodenectomy (PPPD) is unclear. This study aimed to investigate the impact of preoperative PNI on mortality rate and cancer recurrence rate in patients who underwent PPPD.

Methods: A total of 718 patients who were diagnosed with periampullary or pancreatic cancer and underwent PPPD between January 2012 and December 2016 were analyzed.

Clinical Laboratory Parameter-Driven Machine Learning for Participant Selection in Bioequivalence Studies Among Patients With Gastric Cancer: Framework Development and Validation Study.

Background: Insufficient participant enrollment is a major factor responsible for clinical trial failure.

Objective: We formulated a machine learning (ML)-based framework using clinical laboratory parameters to identify participants eligible for enrollment in a bioequivalence study.

Methods: We acquired records of 11,592 patients with gastric cancer from the electronic medical records of Kyungpook National University Hospital in Korea.

Practical significance and assessment of gastric ultrasound in preoperative carbohydrate loading: A retrospective analysis of 746 patients within the enhanced recovery after surgery pathway.

Background: Although preoperative carbohydrate loading (PCL) is generally regarded as safe, the significance of assessing gastric status by gastric ultrasound after PCL application has not been adequately explored in real-world clinical settings. Therefore, this study evaluated gastric status after PCL using gastric ultrasound and its potential role in enhancing patient safety within a large surgical cohort under Enhanced Recovery After Surgery (ERAS) protocols.

Methods: We retrospectively analyzed patients who received PCL and underwent hepatobiliary surgery within ERAS protocol between November 2018 to December 2023.

Machine learning assessment of zoonotic potential in avian influenza viruses using PB2 segment.

Background: Influenza A virus (IAV) is a major global health threat, causing seasonal epidemics and occasional pandemics. Particularly, Influenza A viruses from avian species pose significant zoonotic threats, with PB2 adaptation serving as a critical first step in cross-species transmission. A comprehensive risk assessment framework based on PB2 sequences is necessary, which should encompass detailed analyses of specific residues and mutations while maintaining sufficient generality for application to non-PB2 segments.

Diagnostic Accuracy of a Deep Learning Algorithm for Detecting Unruptured Intracranial Aneurysms in Magnetic Resonance Angiography: A Multicenter Pivotal Trial.

Background: Intracranial aneurysm rupture is associated with high mortality and disability rates. Early detection is crucial, but increasing diagnostic workloads place significant strain on radiologists. We evaluated the efficacy of a deep learning algorithm in detecting unruptured intracranial aneurysms (UIAs) using time-of-flight (TOF) magnetic resonance angiography (MRA).

Python algorithm package for automated Estimation of major legume root traits using two dimensional images.

A simple Python algorithm was used to estimate the four major root traits: total root length (TRL), surface area (SA), average diameter (AD), and root volume (RV) of legumes (adzuki bean, mung bean, cowpea, and soybean) based on two-dimensional images. Four different thresholding methods; Otsu, Gaussian adaptive, mean adaptive and triangle threshold were used to know the effect of thresholding in root trait estimation and to optimize the accuracy of root trait estimation. The results generated by the algorithm applied to 400 legume root images were compared with those generated by two separate software (WinRHIZO and RhizoVision), and the algorithm was validated using ground truth data.

A novel clinical investigation using deep learning and human-in-the-loop approach in orbital volume measurement.

Orbital volume assessment is crucial for surgical planning. Traditional methods lack efficiency and accuracy. Recent studies explore AI-driven techniques, but research on their clinical effectiveness is limited.

Signal-Guided Multitask Learning for Myocardial Infarction Classification Using Images of Electrocardiogram.

Introduction: The diagnosis of myocardial infarction (MI) needs to be swift and accurate, but definitively diagnosing it based on the first test encountered in clinical practice, the electrocardiogram (ECG), is not an easy task. The purpose of the study was to develop a deep learning (DL) algorithm using multitask learning method to differentiate patients experiencing MI from those without coronary artery disease using image-based ECG data.

Methods: A DL model was developed based on 11,227 ECG images.

Diagnostic accuracy of dental caries detection using ensemble techniques in deep learning with intraoral camera images.

Camera image-based deep learning (DL) techniques have achieved promising results in dental caries screening. To apply the intraoral camera image-based DL technique for dental caries detection and assess its diagnostic performance, we employed the ensemble technique in the image classification task. 2,682 intraoral camera images were used as the dataset for image classification according to dental caries presence and caries-lesion localization using DL models such as ResNet-50, Inception-v3, Inception-ResNet-v2, and Faster R-convolutional neural network according to diagnostic study design.

Deep Learning Model Using Stool Pictures for Predicting Endoscopic Mucosal Inflammation in Patients With Ulcerative Colitis.

Introduction: Stool characteristics may change depending on the endoscopic activity of ulcerative colitis (UC). We developed a deep learning model using stool photographs of patients with UC (DLSUC) to predict endoscopic mucosal inflammation.

Methods: This was a prospective multicenter study conducted in 6 tertiary referral hospitals.

Roadmap for providing and leveraging annotated data by cytologists in the PDAC domain as open data: support for AI-based pathology image analysis development and data utilization strategies.

Pancreatic cancer is one of the most lethal cancers worldwide, with a 5-year survival rate of less than 5%, the lowest of all cancer types. Pancreatic ductal adenocarcinoma (PDAC) is the most common and aggressive pancreatic cancer and has been classified as a health emergency in the past few decades. The histopathological diagnosis and prognosis evaluation of PDAC is time-consuming, laborious, and challenging in current clinical practice conditions.

Comparing deep learning and handcrafted radiomics to predict chemoradiotherapy response for locally advanced cervical cancer using pretreatment MRI.

Concurrent chemoradiotherapy (CRT) is the standard treatment for locally advanced cervical cancer (LACC), but its responsiveness varies among patients. A reliable tool for predicting CRT responses is necessary for personalized cancer treatment. In this study, we constructed prediction models using handcrafted radiomics (HCR) and deep learning radiomics (DLR) based on pretreatment MRI data to predict CRT response in LACC.

Tooth caries classification with quantitative light-induced fluorescence (QLF) images using convolutional neural network for permanent teeth in vivo.

Background: Owing to the remarkable advancements of artificial intelligence (AI) applications, AI-based detection of dental caries is continuously improving. We evaluated the efficacy of the detection of dental caries with quantitative light-induced fluorescence (QLF) images using a convolutional neural network (CNN) model.

Methods: Overall, 2814 QLF intraoral images were obtained from 606 participants at a dental clinic using Qraypen C® (QC, AIOBIO, Seoul, Republic of Korea) from October 2020 to October 2022.

Real-time ultrasound guidance versus fluoroscopic guidance in thoracic epidural catheter placement: a single-center, non-inferiority, randomized, active-controlled trial.

Introduction: Fluoroscopy can improve the success rate of thoracic epidural catheter placement (TECP). Real-time ultrasound (US)-guided TECP was recently introduced and showed a high first-pass success rate. We tested whether real-time US-guided TECP results in a non-inferior first-pass success rate compared with that of fluoroscopy-guided TECP.

The analysis of nursing diagnoses determined by students for patients in rehabilitation units.

This study aimed to analyze nursing diagnoses determined by the nursing students for patients in rehabilitation unit. Data were collected from 190 case reports submitted by the nursing students who practiced in the rehabilitation unit, and analyzed on the basis of North American Nursing Diagnosis Association (NANDA) International, Inc. nursing diagnoses.

Comparison of sitting and prone positions for real-time ultrasound-guided thoracic epidural catheter placement: a randomized controlled trial.

Introduction: Real-time ultrasound-guided thoracic epidural catheter placement (US-TECP) has been recently introduced. Patient's position is associated with the success of spine interventions; however, the effects of position on the outcome of the procedure remain unknown. We aimed to assess the clinical usefulness of patient positioning during real-time US-TECP.

Factors Associated With Rebound Pain After Patient-controlled Epidural Analgesia in Patients Undergoing Major Abdominal Surgery: A Retrospective Study.

Objectives: Although patient-controlled epidural analgesia (PCEA) is an effective form of regional analgesia for abdominal surgery, some patients experience significant rebound pain after the discontinuation of PCEA. However, risk factors for rebound pain associated with PCEA in major abdominal surgery remain unknown. This study evaluated the incidence of rebound pain related to PCEA and explored potential associated risk factors.

Use of video-based telehealth services using a mobile app for workers in underserved areas during the COVID-19 pandemic: A prospective observational study.

Background: The COVID-19 pandemic has limited face-to-face treatment, triggering a change in the structure of existing healthcare services. Unlike other groups, workers in underserved areas have relatively poor access to healthcare.

Objective: This study aimed to investigate the effects of video-based telehealth services using a mobile personal health record (PHR) app for vulnerable workers with metabolic risk factors.

Programmed Intermittent Epidural Bolus versus Continuous Epidural Infusion in Major Upper Abdominal Surgery: A Retrospective Comparative Study.

Although recent evidence shows that the programmed intermittent epidural bolus can provide improved analgesia compared to continuous epidural infusion during labor, its usefulness in major upper abdominal surgery remains unclear. We evaluated the effect of programmed intermittent epidural bolus versus continuous epidural infusion on the consumption of postoperative rescue opioids, pain intensity, and consumption of local anesthetic by retrospective analysis of data of patients who underwent major upper abdominal surgery under ultrasound-assisted thoracic epidural analgesia between July 2018 and October 2020. The primary outcome was total opioid consumption up to 72 h after surgery.

A Worker-Centered Personal Health Record App for Workplace Health Promotion Using National Health Care Data Sets: Design and Development Study.

Background: Personal health record (PHR) technology can be used to support workplace health promotion, and prevent social and economic losses related to workers' health management. PHR services can not only ensure interoperability, security, privacy, and data quality, but also consider the user's perspective in their design.

Objective: Using Fast Healthcare Interoperability Resources (FHIR) and national health care data sets, this study aimed to design and develop an app for providing worker-centered, interconnected PHR services.

Quantitative gait analysis of idiopathic normal pressure hydrocephalus using deep learning algorithms on monocular videos.

A vision-based gait analysis method using monocular videos was proposed to estimate temporo-spatial gait parameters by leveraging deep learning algorithms. This study aimed to validate vision-based gait analysis using GAITRite as the reference system and analyze relationships between Frontal Assessment Battery (FAB) scores and gait variability measured by vision-based gait analysis in idiopathic normal pressure hydrocephalus (INPH) patients. Gait data from 46 patients were simultaneously collected from the vision-based system utilizing deep learning algorithms and the GAITRite system.